R. Lynn Kirlin, William J. Done's Covariance Analysis for Seismic Signal Processing PDF

By R. Lynn Kirlin, William J. Done

ISBN-10: 0931830419

ISBN-13: 9780931830419

ISBN-10: 156080081X

ISBN-13: 9781560800811

This quantity is meant to provide the geophysical sign analyst enough fabric to appreciate the usefulness of knowledge covariance matrix research within the processing of geophysical indications. A historical past of easy linear algebra, information, and basic random sign research is thought. This reference is exclusive in that the knowledge vector covariance matrix is used all through. instead of facing just one seismic facts processing challenge and offering numerous tools, the focus during this publication is on just one primary technique - research of the pattern covariance matrix proposing many seismic information difficulties to which the method applies. This quantity could be of curiosity to many researchers, offering a mode amenable to many designated purposes. It bargains a various sampling and dialogue of the idea and the literature built up to now from a standard viewpoint.

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Signal Processing, 503-506. Pillai, S. , 1989, Array signal processing: Springer-Verlag Inc. Scharf, L. , 1991, Statistical signal processing: Addison-Wesley Publ. Corp. 50. org/ 50 Chapter 5 Temporal and Spatial Spectral Analysis R. Lynn Kirlin Spectral analysis is a broad topic. Most scientists and engineers who deal with signals are quite comfortable with the concepts of time-frequency relationships. They are familiar with the Fourier transform and the common theorems such as Parseval’s, convolution, delay, etc.

6) where x is of length M, A is M ϫ r and s(t) is the vector of signals or signal sources which are sensed through the measurement matrix A. 50. org/ 37 pendent. Thus the rank of APs AH is r, where Ps ϭ E{ssH}, the source covariance matrix. 7) is composed of two covariance matrices, APs AH and N, one a result of signals only and the other of noise only. Without knowing the S/N, how might we go about estimating A and Ps? The answers to this depend on a priori knowledge. For example, if we know N, then APs AH ϭ R Ϫ N.

Thus, straightforward application of the above will yield eigenstructures with an unclear demarcation of the signal subspace, because there will be more than one “larger” eigenvalue per complex wavefront present. If the reflections are band-pass filtered to create a more narrowband signal, the model is better matched, but signal energy has been lost. Methods of combining Fourier coefficients are used in broadband extensions. We will discuss these methods and the above problems of model unsuitability in Chapter 6.

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Covariance Analysis for Seismic Signal Processing by R. Lynn Kirlin, William J. Done

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